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Found 12 papers, 8 papers with code
Typhoon: Thai Large Language Models
Typhoon is a series of Thai large language models (LLMs) developed specifically for the Thai language.
Mitigating Word Bias in Zero-shot Prompt-based Classifiers
To address this problem, it is possible to optimise classification thresholds on a labelled data set, however, this mitigates some of the advantages of prompt-based classifiers.
LLM Comparative Assessment: Zero-shot NLG Evaluation through Pairwise Comparisons using Large Language Models
Current developments in large language models (LLMs) have enabled impressive zero-shot capabilities across various natural language tasks.
Can Generative Large Language Models Perform ASR Error Correction?
In this paper we investigate using ChatGPT, a generative LLM, for ASR error correction.
CUED at ProbSum 2023: Hierarchical Ensemble of Summarization Models
In this paper, we consider the challenge of summarizing patients' medical progress notes in a limited data setting.
SelfCheckGPT: Zero-Resource Black-Box Hallucination Detection for Generative Large Language Models
In this work, we propose "SelfCheckGPT", a simple sampling-based approach that can be used to fact-check the responses of black-box models in a zero-resource fashion, i. e. without an external database.
MQAG: Multiple-choice Question Answering and Generation for Assessing Information Consistency in Summarization
In this work, we introduce an alternative scheme based on standard information-theoretic measures in which the information present in the source and summary is directly compared.
Podcast Summary Assessment: A Resource for Evaluating Summary Assessment Methods
The podcast summary assessment data is available.
Sparsity and Sentence Structure in Encoder-Decoder Attention of Summarization Systems
Second, we propose a modified architecture that selects the subset of sentences to constrain the encoder-decoder attention.
Long-Span Summarization via Local Attention and Content Selection
Transformer-based models have achieved state-of-the-art results in a wide range of natural language processing (NLP) tasks including document summarization.
Attention Forcing for Machine Translation
This approach guides the model with the generated output history and reference attention, and can reduce the training-inference mismatch without a schedule or a classifier.
CUED_speech at TREC 2020 Podcast Summarisation Track
Our approach consists of two steps: (1) Filtering redundant or less informative sentences in the transcription using the attention of a hierarchical model; (2) Applying a state-of-the-art text summarisation system (BART) fine-tuned on the Podcast data using a sequence-level reward function.
